Supervised Learning: Classification — Machine Learning Roadmap

Predicting discrete categories or classes from data

Steps in Supervised Learning: Classification

Logistic Regression — intermediate · Modeling the probability of a binary outcome
k-Nearest Neighbors (kNN) — intermediate · Classifying a point based on the labels of its closest neighbors
Decision Trees — intermediate · Splitting data recursively based on feature thresholds to make predictions
Naive Bayes — intermediate · A probabilistic classifier based on Bayes' theorem and a strong independence assumption
Support Vector Machines (SVM) — intermediate · Finding the maximum-margin boundary between classes, with kernels for non-linear data
Classification Evaluation Metrics — intermediate · Accuracy, precision, recall, F1 and ROC-AUC, and when each one matters most